Mark van der Wilk
38 papers · 2014–2025 · 7 conferences · across top CS/AI conferences
Achievements
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π Conference Polyglot (7) π Renaissance Researcher (5) π Interdisciplinary Bridge π§ Keyword Pioneer π Academic Marathon (11)
π
Academic Marathon
(11)
π
Cross-Pollinator
(13)
πΊοΈ
Taxonomy Completionist
(41)
π¬
Deep Specialist
(20)
π
Keyword Champion
(8)
ποΈ
Keyword Collector
(109)
β‘
Prolific Year
(5)
π
Century Club
(38)
β
The Questioner
π₯
Unstoppable
(10)
Conferences
NIPS (17)
ICML (9)
UAI (4)
JMLR (3)
AISTATS (2)
ICLR (2)
IJCAI (1)
Top co-authors
Research topics
Keywords
gaussian process
(14)
variational inference
(14)
marginal likelihood
(8)
data augmentation
(4)
sparse approximation
(4)
inducing variable
(3)
sparse gaussian process
(3)
bayesian model selection
(3)
laplace approximation
(3)
neural network
(3)
uncertainty quantification
(3)
invariance learning
(3)
convolutional neural network
(3)
inducing point
(2)
bayesian inference
(2)
black-box optimization
(2)
bayesian neural network
(2)
bayesian optimization
(2)
dynamical system
(2)
image classification
(2)
Papers
Continuous Bayesian Model Selection for Multivariate Causal Discovery
ICML 2025
Rethinking Aleatoric and Epistemic Uncertainty
ICML 2025
A Meta-Learning Approach to Bayesian Causal Discovery
ICLR 2025
Adjusting Model Size in Continual Gaussian Processes: How Big is Big Enough?
ICML 2025
Transition Constrained Bayesian Optimization via Markov Decision Processes
NIPS 2024
Learning in Deep Factor Graphs with Gaussian Belief Propagation
ICML 2024
Bivariate Causal Discovery using Bayesian Model Selection
ICML 2024
Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees
JMLR 2024
Noether's Razor: Learning Conserved Quantities
NIPS 2024
Actually Sparse Variational Gaussian Processes
AISTATS 2023
Stochastic Marginal Likelihood Gradients using Neural Tangent Kernels
ICML 2023
Learning Layer-wise Equivariances Automatically using Gradients
NIPS 2023
Learning invariant weights in neural networks
UAI 2022
Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations
NIPS 2022
Memory safe computations with XLA compiler
NIPS 2022
Relaxing Equivariance Constraints with Non-stationary Continuous Filters
NIPS 2022
SnAKe: Bayesian Optimization with Pathwise Exploration
NIPS 2022
Last Layer Marginal Likelihood for Invariance Learning
AISTATS 2022
Bayesian Neural Network Priors Revisited
ICLR 2022
Data augmentation in Bayesian neural networks and the cold posterior effect
UAI 2022
Correlated weights in infinite limits of deep convolutional neural networks
UAI 2021
Deep Neural Networks as Point Estimates for Deep Gaussian Processes
NIPS 2021
Speedy Performance Estimation for Neural Architecture Search
NIPS 2021
Tighter Bounds on the Log Marginal Likelihood of Gaussian Process Regression Using Conjugate Gradients
ICML 2021
The promises and pitfalls of deep kernel learning
UAI 2021
Convergence of Sparse Variational Inference in Gaussian Processes Regression
JMLR 2020
Stochastic Segmentation Networks: Modelling Spatially Correlated Aleatoric Uncertainty
NIPS 2020
A Bayesian Perspective on Training Speed and Model Selection
NIPS 2020
Bayesian Layers: A Module for Neural Network Uncertainty
NIPS 2019
Rates of Convergence for Sparse Variational Gaussian Process Regression
ICML 2019
Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models
ICML 2019
Scalable Bayesian dynamic covariance modeling with variational Wishart and inverse Wishart processes
NIPS 2019
Learning Invariances using the Marginal Likelihood
NIPS 2018
Convolutional Gaussian Processes
NIPS 2017
Concrete Problems for Autonomous Vehicle Safety: Advantages of Bayesian Deep Learning
IJCAI 2017
GPflow: A Gaussian Process Library using TensorFlow
JMLR 2017
Understanding Probabilistic Sparse Gaussian Process Approximations
NIPS 2016
Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models
NIPS 2014